Social Media Information Classification of Earthquake Disasters Based on BERT Transfer Learning Model

Objectives: With the rapid development of the Internet, social media has become an important information source of emergency events. However, there are a lot of duplication, errors and even malicious contents in social media, which need to be effectively classified to provide more accurate information for di⁃ saster emergency response. Methods: Deep learning has greatly improved the accuracy and efficiency of text classification. This paper takes earthquake disaster as an example, and builds a multi-label classifica⁃ tion model based on bidirectional encoder representation from transformers (BERT) transfer learning. Over 50 000 posts about 5 earthquakes are collected as training samples from SINA Weibo, which is a very popu⁃ lar social media in China. Each sample is manually marked as one or more labels, such as hazards informa⁃ tion, loss information, rescue information, public opinion information and useless information. Results: By fine-tune training, the classification accuracies of the proposed model on training dataset and test dataset reach 97% and 92%, respectively. The area under curve score of each label ranges from 0.952 to 0.998. Conclusions: The results prove that the multi-label classification using BERT transfer learning is of high reliability. The proposed model can be applied to the emergency management services for earthquake events, which is beneficial for the rapid disaster rescue and relief. © 2024 Editorial Department of Geomatics and Information Science of Wuhan University. All rights reserved.